Energy-related information presentation system

ABSTRACT

A system and approach for diagnostic visualizations of, for example, building control systems data. A focus may be on a similarity metric for comparing operations among sites relative to energy consumption. Normalizing factors may be used across sites with varying equipment consumption levels to be compared automatically. There may also be a high level overview of an enterprise of sites. For instance, consumption totals of the sites may be normalized by site size and length of time of a billing period to identify such things as outlier sites. One may use a main view of geographic distribution dynamically linked to subviews showing distribution by size, by aggregated climate, and so on. With these views, one may quickly drill through the enterprise and identify sites of interest for further investigation. A key metric may be intensity which invokes viewing virtually all sites by normalized consumption for a unit amount of time.

This application is a continuation of U.S. Non-provisional ApplicationSer. No. 14/059,364, filed Oct. 21, 2013, which in turn is acontinuation of U.S. Non-provisional Application Ser. No. 13/015,545,filed Jan. 27, 2011, and entitled “An Energy-Related InformationPresentation System”, now U.S. Pat. No. 8,577,505, issued Nov. 5, 2013,which claims the benefit of U.S. Provisional Application Ser. No.61/336,789, filed Jan. 27, 2010, and entitled “Integrated Multi-SiteEnergy Dashboard”. U.S. Non-provisional Application Ser. No. 13/015,545,filed Jan. 27, 2011, and U.S. Provisional Application Ser. No.61/336,789, filed Jan. 27, 2010, are hereby incorporated by reference.U.S. Non-provisional Application Ser. No. 14/059,364, filed Oct. 21,2013, is hereby incorporated by reference.

BACKGROUND

The present disclosure pertains to energy usage and particularly to anapparatus and approach for displaying energy-related information.

SUMMARY

The disclosure reveals a system and approach for diagnosticvisualizations of, for example, building control systems data. A focusmay be on a similarity metric for comparing operations among sitesrelative to energy consumption. Normalizing factors may be used acrosssites with varying equipment consumption levels to be comparedautomatically. There may also be a high level overview of an enterpriseof sites. For instance, consumption totals of the sites may benormalized by site size and length of time of a billing period toidentify such things as outlier sites. One may use a main view ofgeographic distribution dynamically linked to subviews showingdistribution by size, by aggregated climate, and so on. With theseviews, one may quickly drill through the enterprise and identify sitesof interest for further investigation. A key metric may be intensitywhich invokes viewing virtually all sites by normalized consumption fora unit amount of time.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a diagram of an apparatus used in conjunction withaccomplishing various aspects presented in the present disclosure;

FIG. 2 is a diagram of a processor with a display and user interface,connected to an enterprise of sites;

FIGS. 3 and 4 are diagrams of activity for energy-related informationpresentation systems;

FIG. 5 is a diagram of a dashboard-oriented energy-related informationpresentation approach;

FIG. 6 is a diagram of a formula for calculating an alpha factor;

FIG. 7 is a diagram of a formula for calculating a beta factor;

FIG. 8 is a diagram of an example an alpha calculation and view;

FIG. 9 is a diagram of a formula for calculating another alpha factor;

FIGS. 10a and 10b are tables of alpha and beta calculations,respectively, for various sites;

FIG. 10c is a table of distances of other sites nearby a noted site ofinterest in FIGS. 10a and 10 b;

FIGS. 11a and 11b are diagrams of alpha calculations for various sitesfor roof top units and lights, respectively;

FIG. 12 is a diagram of a daily lighting and heating, ventilation andair conditioning system profile;

FIG. 13 is a diagram of a heating, ventilation and air conditioningcalendar;

FIG. 14 is a diagram of a lighting calendar; and

FIGS. 15-18 are diagrams of screen shots of an approach utilizing a keymetric of intensity to view customer sites by normalized consumption.

DESCRIPTION

FIG. 1 illustrates an example apparatus 100 for obtaining, processingand displaying energy-related information according to the presentdisclosure. Other examples of the apparatus 100 may be used. Apparatus100 may be used to provide graphical user interfaces, visualizations anddashboards for displaying energy-related information according to thepresent disclosure. Other kinds of graphical user interfaces,visualizations and dashboards may be provided by apparatus 100.

As shown in FIG. 1, the apparatus 100 may incorporate a processingsystem 102 for processing energy-related data and generating graphicaldisplays. The term “energy” may represent any suitable utility, such aselectricity, gas, fuel oil, cold water, hot water, steam, or the like.The processing system 102 in this example may incorporate at least oneprocessor 104, at least one network interface 108, and at least onememory 106. The processor 104 may process the energy-related data andgenerate the graphical displays. The processor 104 may incorporatevirtually any suitable processing or computing component.

Memory 106 may be coupled to the processor 104. The memory 106 may beused to store instructions and data used, generated, or collected by theprocessor 104. The memory 106 may, for example, store the energy-relateddata collected and analyzed by the processor 104 and analysis resultsgenerated by the processor 104. The memory 106 may represent a suitablevolatile and/or non-volatile storage and retrieval device or devices.

The network interface 108 may support communication with externalcomponents, such as an external database or external sensors. Thenetwork interface 108 may, for example, receive temperature readingsfrom sensors, energy usage readings from meters, or any other oradditional energy-related data. The network interface 108 mayincorporate virtually any suitable structure for facilitatingcommunications over one or more networks, such as an Ethernet interfaceor a wireless transceiver. Other connections may be accomplished with anexternal connections module 116.

At least one item or display 110 may be coupled to the processing system102. The display 110 can present various kinds of information to one ormore users. For example, the display 110 could present one or moregraphical user interfaces containing graphs and/or other informationrelated to energy usage. This may allow, for example, energy analysts orother personnel to review the analysis results and identifyenergy-related issues with an enterprise or other entity. Item 110 mayrepresent any suitable display device, such as a liquid crystal display(LCD), cathode ray tube (CRT) display, light emitting diode (LED)display, or other type of visual information providing mechanism.

In the present examples, the processor 104 may perform various functionsfor supporting the collection and analysis of energy-related data. Forexample, the processor 104 may support data input/output (I/O) functionswith a data I/O module 114 to support communication with othercomponents, such as input devices (like a mouse or keyboard) at a userinterface 117 and output devices (such as display 110). Processor 104may also perform collection functions with collection module 112 anddetection mechanism 115 to collect data related to the energy usage ofone or more enterprises. Processor 104 may further perform operationsand functions at an analysis module 113 to analyze collected data, suchas cost-savings calculations and normalization functions, and performother analyses and calculations. In addition, processor 104 may performgraphical user interface generation functions at GUI generation module111 to generate one or more graphical user interfaces for presentationto one or more users. The contents of the generated graphical userinterfaces may depend, at least in part, on the analysis performed byvarious portions of the processor 104. Example graphical userinterfaces, graphs, tables, maps and the like are illustrated herein.Each of these graphical presentations, visualizations, dashboards, andthe like may be implemented using any suitable hardware, software,firmware, or combination thereof, shown in FIG. 1.

The apparatus 100 shown in FIG. 1 may be used in a larger system, suchas a process control system used to control one or multiple industrialfacilities. In these arrangements, apparatus 100 may communicate withsensors, controllers, servers, or historian mechanisms in the processcontrol system to gather data for analysis. These communications mayoccur over Ethernet or other wired or wireless network or networks.Also, in the illustrative examples, apparatus 100 may represent anysuitable device in the process control system, such as a server oroperator station. In other illustrative examples, the apparatus 100 mayanalyze data from multiple enterprises, and data for each enterprise maybe provided to the apparatus 100 or retrieved by the apparatus 100 inany suitable manner.

In one aspect of operation, the apparatus 100 may analyze energy-relateddata and provide graphical interfaces and presentations based on theanalyses to energy analysts or other personnel. For instance, apparatus100 may receive and analyze data associated with various enterprises,such as for an entity having multiple individual locations or sites.Also, apparatus 100 may be used to analyze any suitable energy-relatedaspects of that domain, such as energy financial costs, parameters, andso forth as indicated herein.

In some illustrative examples, apparatus 100 may provide improved datavisualizations (graphical displays) for energy analysts or other users,which may be useful in detecting and diagnosing issues in energy use.For instance, a visualization may integrate reports and graphs used by auser into a single interactive display. Depending on an implementation,such visualization may involve an integration of different displays,linking of symbols to detailed information for specific sites (areas,shapes, colors, shades, symbols, and so on associated with energyusage), integration of histories, linking of views, and providingtime-based views. Shades may be instances of a grayscale or variants ofan intensity of a displayed color such as a grey.

Apparatus 100 may also use a set of performance metrics in the datavisualizations, where the metrics serve to highlight potential energyuse issues at a site or other place. A user may be able to select one ofthose measures, which may then be used to drive an integrated display ofcharts. These metrics can be applied to analyze energy performance overa user-selectable period of time.

Although FIG. 1 illustrates an example apparatus 100 for displayingenergy-related information, various changes may be made to theapparatus. For example, the apparatus 100 may include any number ofprocessing systems, processors, memories, and network interfaces. Also,the apparatus 100 may be coupled directly or indirectly to any number ofdisplays, and more than one apparatus 100 may be used in a system. Inaddition, FIG. 1 illustrates one example operational environment wherethe processing of energy-related data may be used. This functionalitycould be used with any other suitable device or system.

FIG. 2 is a diagram of processor 104, with display 110 and userinterface (UI) 117, connected to an enterprise 125 of n sitesincorporating sites 121, 122, and 124 which represent site 1, site 2,and additional sites through site number n, respectively. Each site mayhave a detection mechanism 115 connected to it. Mechanism 115 may obtaindata relative to each of the respective sites, pertaining for instanceto energy consumption and the like.

FIG. 3 is a diagram of example basic activity of an energy relatedinformation presentation system. This activity may be performed byapparatus 100 or other mechanism. Symbol 131 indicates obtaining data onenergy consumption at site equipment. Example equipment may incorporateheating, ventilation and air conditioning (HVAC), and lighting. The datamay be normalized with a dual layer approach using alpha and betafactors, as indicated in symbol 132. The normalized data may be used tocompare sites as indicated in symbol 133. The comparison of sites mayaid, as indicated in symbol 134, in detecting abnormalities across anenterprise of sites.

FIG. 4 is a diagram of activity of an energy-related informationpresentation system. This activity may be performed by apparatus 100 orother mechanism. Using a processor with a display may provide avisualization to support identification of issues of a site among sites,as indicated in symbol 141. Symbol 142 indicates using alpha and betafactors to drive a specific site of interest. Then there may be agenerating of views of a highest priority site having the most and/orlargest issues related to energy consumption of an HVAC and/or lighting,as noted in symbol 143. A linking to a calendar view of energy usage forvarious periods of time and profile views of HVAC and/or lighting energyusage at a site level, and optionally incorporating weather data may beperformed, as indicated by symbol 144. According to symbol 145, theremay be a scrolling and/or selecting through time across sitesindividually or together.

FIG. 5 is a diagram of activity for a dashboard oriented energy-relatedinformation presentation approach. This activity may be performed byapparatus 100 or other mechanism. A display of a processor may providean intensity map having a dashboard, as indicated by symbol 151. Symbol152 may note using views of one or more energy consuming sites on ageographic map with an energy consumption metric coded with symbols viashape, size, shade, color, symbol, and/or other graphical distinction toidentify energy consumption amounts in an absolute, relative and/ornormalized manner. There may be a use of linking views with informationabout one or more energy consuming sites where the information mayincorporate geographical distribution, distribution by size,distribution by aggregated climate zone, distribution by energyconsumption, and/or so on, across an enterprise of sites, as indicatedin symbol 153. Symbol 154 notes that there may be a making of selectionsin virtually any of all windows. A mouseover in virtually all windowsmay provide details of each site incorporating location of energyconsumption, information about billing associated with the energyconsumption, and so on. A window may be a screen or graphicalpresentation. One or more windows may be on a display at the same timeor at different times.

Various Figures herein illustrate example graphical user interfaces,visualizations and dashboards for displaying energy-related informationaccording to the present disclosure. Other kinds of graphical userinterfaces, visualizations and dashboards may be used.

Energy analysis services may be provided for customers that havemultiple sites located across the country. There may be an effort toprovide recommendations on how to better operate these sites, using acombination of utility bill data, electric or other utility meter data,control system operational data, and weather conditions. A challenge inproviding these services may be in sifting through a massive amount ofdata to identify actionable recommendations that can be implemented atthe customer's site, and to perform this activity in a cost effectivemanner.

Diagnostic visualizations for building control systems data may benoted. A present approach may address analyzing the HVAC and lightingsystems at an individual site, and comparing their performance againstother sites and/or comparing them over time at the same site.

A focus of the approach may be on a development of a similarity metricto compare operations between sites, and visualizations to support anenergy analyst in quickly identifying sources with issues in the HVACand lighting systems.

The present approach may have a definition of normalizing factors acrosssites, so that sites with varying equipment levels can be comparedautomatically. These normalizing factors may be called alpha and beta,and be defined on a per site basis. There may be an approach forvisualizing the normalizing factors.

There may be a use of HVAC and lighting data for a single site in acalendar view. This view may allow an analyst to quickly assessperformance over time, and compare same day performance for the samesite. This view may facilitate an assessment of whether an issue ispersistent or sporadic. Other approaches may look at individual trendplots.

There may be an incorporation of HVAC and lighting data into a “birthdaycake” view for each day. This view may allow an analyst to develop acharacteristic profile for a site, and use this characteristic profileas a comparison within sites and between sites.

In the present disclosure, one may have an approach to normalize HVACand lighting operations between sites. Essentially, one may define afactor, called an alpha (α) factor, for each stage of lighting or HVACequipment, and for each piece of equipment at the site. This may requirethat data be available in a form that separates the pieces of equipmentand stages of operation. Then, for each of these stages and pieces ofequipment, one may define a daily period of operation, such asunoccupied hours; and an aggregation period, such as one month. Thealpha factor may then be used to calculate the percentage of thoseoperation and aggregation periods where this stage of equipment/lightingwas activated.

Another step or stage of normalization may invoke collecting virtuallyall of the alpha factors for a single site, and then normalizing them bya number of pieces of equipment at that site. The normalization may bereferred to as a beta ((β) factor. For example, one may have alphafactors for eight rooftop HVAC units at one site, and six rooftop HVACunits at another site. To normalize between sites, one may sum the alphafactors and divide by the number of units at each site. Thus, the betafactor may be the fraction of time that that total site capacity wasactivated during the aggregation period.

An example of a calculation for alpha and beta factors, and an exampleof a visualization of alpha calculations across sites, are shown herein.

The alpha and beta factors may be intended to either provide anautomated metric for comparison, or to assist the analyst in identifyingsites that are candidates for a further drill down.

FIG. 12 shows charts with site details with daily profiles 27 and 28 forHVAC and lighting, respectively. FIG. 13 shows an HVAC calendar 31 withsite details. FIG. 14 shows a lighting calendar 32 with site details.FIGS. 12, 13 and 15 are data instances with rough accuracy as examplesfor illustrative purposes.

Once a site is selected for the further drill down, the analyst may needviews to support rapid identification of specific issues in the HVAC andlighting systems. FIG. 13 shows the HVAC calendar 31 for a specific site(2507). This visualization may be used to illustrate the operation ofthe HVAC systems across the aggregation period. In the view shown here,one may see the operation of a single site across a one month period,and then use this view to identify when heating, cooling and fan stagesare operating across the aggregation period. This may allow an analystto see an entire month's data in a single view, and rapidly identifyoperational issues such as running HVAC systems during unoccupiedperiods. A similar approach with a calendar 32 for lighting systems isshown in FIG. 14.

Eventually, a daily detail view may be used as a bottom level drill downinto the data. As noted herein, FIG. 12 may show the profiles 27 and 28with details summarized in the HVAC and lighting calendars 31 and 32 inFIGS. 13 and 14, respectively, but at a finer level of detail for asingle day, with each subsystem charted individually.

One approach may involve a monthly site review. One may find n outliersites via meter data, utility bill data, or a “big three report”. Foreach of these outliers, the following items may be done. 1) One may findm “similar” reference sites. A similarity metric may currently be adistance between postal codes. One may also incorporate a number ofRTUs, total RTU tonnage, square footage. 2) The similarity metric may beprecomputed and stored in a file in prototype, which could be a table inthe warehouse or other place. 3) The RTU data may be pulled up for thecomparison site, and for the m reference sites. For instance, total runtime may be evaluated during unoccupied periods, total run time may beevaluated for all RTUs during occupied periods, and/or the metric may becomputed on an unoccupied comparison vs. reference and/or occupiedcomparison vs. reference. 4) Lighting data may be pulled up for thecomparison site, and for the m reference sites. A similar evaluation maybe done as for the RTUs. 5) The results may be comparison metrics, suchas RTU run time (occupied, unoccupied), LIGHTS run time (occupied,unoccupied) for each site, and so forth. An approach may incorporateexamining how the total run times for this site compare to the referencesites. 6) Visualization of a comparison and selected reference site maybe shown. An approach here may incorporate examining how the total runtimes for this site compare to those of the reference sites. Examplesmay pertain to RTU stages (heat & cool), fan status, lighting status,and so forth. RTU may be referred to a rooftop unit associated with anHVAC system.

An approach for normalizing RTU log data may be noted. A way tonormalize the RTU data may be needed, so that one can compare acrossstores. This approach may be done in two stages: 1) Normalizing at theequipment/unit level; and 2) Normalizing by total site capacity. Anassumption may be to work with a single point for each normalizedcalculation, e.g., COOL 1. One may define α_(i,j,k)=sum of run time forsite i, stage j, RTU k, in percent, across a specified time of day anddate range for a specific RTU divided by 100 percent*n hours*n daysaccording to a formula 11 in FIG. 6. This formula may represent thefraction of time in the specified period, where this stage or fan wasrunning on a single RTU.

One may define β_(i,j)=the sum of all α_(i,j,k) for a site divided bythe total number of RTUs at this site. A formula 13 for the beta ((β)calculation is shown in FIG. 7. This may be the fraction of time thatthe total site capacity for that stage which was on during the specifiedperiod. One may then compare beta factors across sites. A beta factorshould virtually always have an associated time period and RTU stage.For instance, there may be a time range (midnight to 7 AM) and a state(unoccupied).

An example an alpha “a” calculation and view may be considered. In FIG.8, site 273 at location 14 of the Figure may be compared with nearbysites 15, for instance, over the month of November and at a periodbetween midnight and 9 am. A size of a circle may be proportional to thetotal amount of time running during this period in the date range, whichmay be a numerator of an alpha “α” calculation with formula 11 in FIG.6. A question of which equipment is running for what fraction of thetime and what stage is running may be asked. It may be seen that fanstages run regularly, with the “RTU10” running roughly 27 percent of thetotal time during this period, as indicated by a dot 16 andcorresponding scale 17. SITE_ID (color or shade) and sum of AlphaRTU(size) may be broken down by NTT_NM vs. SITE_ID and DL_PNT_NM. The datamay be filtered on a sum of LOG_VAL_FLT, which includes values greaterthan or equal to 5.

An approach for normalizing lighting log data may be considered. As withthe RTU data, there may be a need for a way to normalize the lightingdata, so that one can make a comparison across stores. One may assume towork with a single point for each lighting category, such as, forexample, employee lights. One may define α_(i,j)=sum of run time forsite i and lighting category j, in percent, across a specified time ofday and date range for a specific lighting category divided by 100percent*nhours*ndays as shown in the formula 12 of FIG. 9. This formulamay represent the fraction of time in the specified period, where thislighting category was on. One may define β_(i,j)=the sum of all α_(i,j)for a site divided by the total number of lighting categories for sitei. This may indicate the fraction of time that the total site lightingwas on during the specified period. One may then compare “0” factorsacross the sites and lighting categories. A “0” factor may virtuallyalways have an associated time period and lighting category. There maybe, for example, a time range (e.g., midnight to 7 AM) and a state(e.g., unoccupied).

Matlab™ may be used to calculate alpha (α) and beta (β) for the varioussites as shown in FIGS. 10a and 10 b, respectively. Example alphacalculations 21 for run hours may be made for site having an ID of 2507(i.e., site 2507) and other sites, e.g., July 20XX, hours 12 AM-7 AM.Similarly, beta calculations 22 may be made relative to the same sites.FIG. 10c is a table 20 of distances of other sites nearby site 2507.Information particularly related to site 2507 may be noted in FIGS.12-14.

FIGS. 11a and 11b are views 23 and 24 of a calculations for varioussites for RTU alpha and lighting alpha, respectively. For instance, site2507 is shown at portion 25 of FIG. 11a in a darker shade with sizes ofcircles proportional to alpha (α). It may be noted that these resultsare not necessarily normalized for a number of RTUs. Observations may beof RTUs with significant run times and employee lighting withsignificant run times.

An approach may address a first step in identifying actionablerecommendations—using the available data most effectively to identifyand drill down to specific sites with energy conservation opportunities,with FIGS. 15-18 being considered.

The approach may provide a high level overview of the enterprise, basedon a key metric selected by an analyst. An analyst may use monthlyconsumption totals normalized by site size and number of days in thebilling period to identify outlier sites using a linked view. The mainview may show the enterprise locations mapped geographically, with thekey metric and site size mapped to a color or shade, and a shape of theicon representing each site. The main view may also be dynamicallylinked to multiple subviews that allow the user to simultaneously viewthe metric of interest cast onto multiple dimensions, such as sizegroup, the climate group, and an overall histogram of the key metric.

With the multiple linked views, the analyst can quickly drill through anenterprise, and identify sites of interest for further investigation.Other approaches may use multiple static tables to rank sites, and thepresent approach may be differentiated from the others by both thegeographic view and the linking of multiple subviews for an additionaldimension.

FIGS. 15-18 are diagrams of screen shots of an approach noted herein. Akey metric may be the intensity—viewing virtually all customer sites bynormalized monthly consumption.

Normalized by square footage, a number of days in the billing period,and so forth, may result in kWh/SF/Day as a key metric. For the mainview and virtually all subviews, the key metric may be encoded to acolor or shade scale shown in the upper right hand corner of each of theFIGS. 15-17. The main views 33, 34 and 35, respectively, show ageographic distribution, with multiple subviews 41, 42 and 43 showingthe distribution by size, distribution by aggregated climate zone, andoverall intensity distribution across virtually all sites, respectively.One may select sites in any window for highlighting across windows.

One may scope out climate and consumption zones by several steps as inthe following. There may be prioritization shown in FIG. 15 with abilling example in terms of a map 33 and graphs. One type of overviewmay be an intensity map which reveals viewing virtually all customersites by normalized monthly consumption. Consumption may be normalizedby square footage, number of days in billing period such as bykWh/SF/Day. There may be the geographic distribution, intensitydistribution by size, intensity distribution by climate zone, andoverall intensity distribution in subviews 41, 42 and 43, respectively,of FIGS. 15-17. Identification of sites may be allowed for furtherinvestigation. Sites may be selected in any window for highlightingacross windows. A mouseover in any window may give site details, such aslocation, size, details on consumption and billing period, and so on.

FIG. 16 shows an example of a selection by climate zone in subview 42one of the subviews, and the resulting linked highlighting across otherviews. This concept may be known as yoking. What may be noted in thepresent approach is not necessarily the concept of yoking, but ratherthe use of the enterprise energy data, combined with site location andother site specific information, to aid an analyst in the task ofidentifying sites of interest for further investigation.

FIG. 16 is a map 34 and graphs which illustrate selection by climatezone. A climate zone window 42 may be used to drive selections. One maysee sites of interest across map 34 and size distribution. Similaryoking may be done across subplots.

FIG. 17 shows an example of narrowing down to a specific site ofinterest, based on this site being an outlier in its climate zone. Theclimate view is shown in the lower middle window 42, and the mostsignificant outlier for this climate zone may be selected. One may seethat by selecting the site in the climate view, one will have identifiedits geographic location, and one can see how that site may rank in theoverall distribution in the lower right hand window. One may also seehow that site ranks compared to sites of similar size in the lower lefthand window.

A mouseover in virtually any window may give site details (location,size, details on consumption & billing period)

FIG. 17 is a map 35 and graphs showing how to narrow down to a site ofinterest. For instance, a question about where the high consumptionzones in climate zone 5 are located may be asked. One may look at ameter and EMS details, and then compare these sites to nearby sites tounderstand the causes for higher consumption. It appears that a topconsumer in zone 5 is site 2507, which may be located for instance inTotowa, N.J. Normalizing calculations may be used to highlightdifferences, and then one may drill down to store level details, as thesites may represent, for example, stores of a chain. A closer view ofmap 35 is shown in FIG. 18.

The following may be a recap. There may be automated anomaly detectionbased on normalization (alpha and beta), along with drill down toHVAC/lighting details. There may be a dual layer approach tonormalization across sites, using logical data to build thenormalization. This may incorporate the alpha and beta factors asdefined herein, and this act to normalize for multiple instances ofequipment within a site (e.g., multiple rooftop units).

The alpha factor may be used to normalize against “expected” operation.The alpha factor may aggregates equipment run time during a specifiedcondition (e.g., unoccupied) over a specified period (e.g., one month).

The beta factor may aggregates for virtually all equipment on the siteand normalizing based on total site capacity. The beta factor mayprovide an approach to compare sites against one another, by normalizingthe aggregated alpha factors by a count of equipment.

The normalization may be based generally only on the content of thedata, not other external factors. Alpha and beta factors may beoperational measures driven by the content of the HVAC and lightingdata, intended to evaluate abnormalities in operational proceduresacross a large enterprise.

Visualization may be to support rapid identification of specificproblems by a human user. The visualization may be used with or withoutthe alpha/beta factors. In the case where alpha/beta factors areavailable, the alpha/beta factors may be used in several ways. First,the alpha/beta factors may be used to drive the user to a specific siteof interest, and automatically generate views of the highest prioritysite. Second, the alpha/beta factors may be used to supplement the rawHVAC/lighting information and provide an approach for a human user toquickly compare a single site against other similar sites.

A specific element of the visualization may be a link to a calendar viewfor comparison across days of week and weeks of the month and weeks ofthe year. The content of the calendar view may be lighting data, HVACdata, or a combination of both. The calendar view may also includeweather data. The calendar view may have scrolling and selectioncapability to support quick navigation through time and across sites.

Another specific element of the visualization may be a link to adetailed daily profile view for analysis of the operation of specificpieces of equipment at the site level. This view may incorporate asimultaneous overview of lighting and HVAC data for grouped lightingfunctions and for specific HVAC units. The view may highlight individualoperating stages for each piece of HVAC equipment over a daily period.The view may incorporate a capability to scroll through time for aspecified site.

An intensity map may be noted. A dashboard may be for viewing multipleenergy consuming sites where an energy consumption metric is presentedon a geographic map and the energy consumption metric is coded via shapeand/or size and/or color to identify largest deviations in the metric.The dashboard may also incorporate one or more linking views thatprovide the user with contextual information, such as geographicdistribution, distribution by size, distribution by aggregated climatezone, distribution across all sites to show consumption in the overallcontext of the enterprise.

The dashboard may also provide an ability to make selections in anywindow and have that selection linked across all windows. Mouseovers invirtually all windows may provide additional contextual details for eachsite relevant to energy consumption, such as location, size, details onenergy consumption and the associated billing period.

A relevant document may be U.S. patent application Ser. No. 12/259,959,filed Oct. 28, 2008, and entitled “Apparatus and Method for DisplayingEnergy-Related Information.” U.S. patent application Ser. No.12/259,959, filed Oct. 28, 2008, is hereby incorporated by reference.

A relevant document may be U.S. patent application Ser. No. 12/483,433,filed Jun. 12, 2009, and entitled “Method and System for Providing anIntegrated Building Summary Dashboard”. U.S. patent application Ser. No.12/483,433, filed Jun. 12, 2009, is hereby incorporated by reference.

In the present specification, some of the matter may be of ahypothetical or prophetic nature although stated in another manner ortense.

Although the present system and/or approach has been described withrespect to at least one illustrative example, many variations andmodifications will become apparent to those skilled in the art uponreading the specification. It is therefore the intention that theappended claims be interpreted as broadly as possible in view of theprior art to include all such variations and modifications.

1-6. (canceled)
 7. An energy-related information presentation systemcomprising: a processor; and one or more detectors for obtaining data oninstances of heating, ventilation and air conditioning and/or lightingequipment at one or more sites of an enterprise; and wherein: the one ormore detectors are connected to the processor; the processor receivesthe data from the one or more detectors on instances of the heating,ventilation and air conditioning and/or lighting equipment at the one ormore sites; the processor outputs a normalization of the equipmentacross the one or more sites based on data on instances of theequipment; an instance of the equipment is an TRU; the normalizationcomprises a directly measured quantity of energy units divided by aproduct of a square footage of an energy consumption site and a numberof days in a billing cycle represented by the directly measured quantityof energy units; and a normalization output by the processor iscalculated in units of kWh/SF/Day.
 8. The energy-related informationpresentation system of claim 7, wherein the processor outputs automatedanomaly detection based on the normalization.
 9. The energy-relatedinformation presentation system of claim 8, wherein the processorevaluates abnormalities in operational procedures of instances ofequipment across an enterprise at the one or more sites of theenterprise.
 10. The energy-related information presentation system ofclaim 9, wherein the processor provides normalizations based on a totalcapacity of the equipment at a site.
 11. The energy-related informationpresentation system of claim 10, the processor compares sites againstone another using normalizations for each site.
 12. The energy-relatedinformation presentation system of claim 7, wherein: the data from theone or more detectors provide a basis for a drill down to details of theheating, ventilation and air conditioning and/or lighting equipment; andthe processor provides a normalization of the equipment across the oneor more sites, using data of instances of the equipment to build thenormalization.
 13. The energy-related information presentation system ofclaim 7, wherein the processor compares sites against one another, witha normalization of an aggregation of the number of days in a billingcycle represented by the directly measured quantity of energy units by acount of instances of equipment for each site.
 14. An energy-relatedinformation presentation system comprising: a processor; and one or moredetectors for obtaining data on instances of heating, ventilation, andair conditioning and/or lighting equipment at one or more sites of anenterprise; and wherein: the one or more detectors are connected to theprocessor; the processor receives the data from the one or moredetectors on instances of the heating, ventilation and air conditioningand/or lighting equipment at the one or more sites; the processoroutputs normalizations of the equipment across the one or more sitesbased on data on instances of the equipment; the data from the one ormore detectors provide a basis for a drill down to details of theheating, ventilation and air conditioning and/or lighting equipment; theprocessor provides normalizations of the equipment across the one ormore sites, using data of instances of the equipment to build thenormalizations; the normalizations each comprise a directly measuredquantity of energy units divided by a product of a square footage of anenergy consumption site and a number of days in a billing cyclerepresented by the directly measured quantity of energy units; andnormalizations output by the processor are calculated in units ofkWh/SF/Day.
 15. The energy-related information presentation system ofclaim 14, wherein the processor provides the normalizations based on atotal capacity of the equipment at a site.
 16. The energy-relatedinformation presentation system of claim 14, the processor comparessites against one another using the normalizations for each site. 17.The energy-related information presentation system of claim 14, whereinthe normalizations are used by the processor to evaluate abnormalitiesin operational procedures across an enterprise of instances of equipmentat the one or more sites of the enterprise.
 18. The energy-relatedinformation presentation system of claim 14, further comprising: adisplay connected to the processor; and wherein: the display provides avisualization to support identification of certain issues of a siteamong sites by a human user of the sites; and a site comprises energyconsuming equipment.
 19. The energy-related information presentationsystem of claim 18, wherein: the visualization includes normalizationfactors to drive the human user to a specific site of interest, andautomatically generate views of a highest priority site; the highestpriority site is the specific site of interest having a largest numberand/or biggest issues relative to the sites by the human user of thesites; and an interest, a priority and/or an issue relates to energyconsumption at a site.
 20. The energy-related information presentationsystem of claim 19, wherein normalization factors are used to provide anapproach for the human user to compare a single site against othersites.
 21. The energy-related information presentation system of claim18, wherein: a first element of the visualization is a link to acalendar view for comparison of sites across days of week, weeks of amonth and/or weeks of a year; and a second element of the visualizationis a link to a detailed daily profile view for analysis of an operationof pieces of energy consumption equipment at a site level.
 22. Theenergy-related information presentation system of claim 21, wherein thecalendar view further comprises weather data and a scrolling and/orselection capability to provide navigation through time and acrosssites.
 23. The energy-related information presentation system of claim21, wherein: the calendar view comprises data of lighting and/or data ofheating, ventilation and air conditioning of the energy consumingequipment; and the detailed daily profile view comprises a simultaneousoverview of data of heating, ventilation and air conditioning and/or oflighting of pieces the energy consuming equipment.
 24. Theenergy-related information presentation system of claim 21, wherein: thedetailed daily profile view further comprises highlights of individualoperating stages of heating, ventilation and air conditioning equipmentand/or lighting of each piece of energy consuming equipment over a dailyperiod; and a capability for a user to scroll through time for aspecific site having stages of heating, ventilation and air conditioningequipment and/or lighting at a level of sites.
 25. The energy-relatedinformation presentation system of claim 18, further comprising: adashboard provided by the display; and wherein the dashboard has one ormore windows for viewing one or more energy consuming sites where anenergy consumption metric is presented on a geographic map and theenergy consumption metric is coded via shape, size, shade and/or colorto identify certain deviations in the energy consumption metric.
 26. Theenergy-related information presentation system of claim 25, wherein: thedashboard further comprises one or more linking views that providecontextual information about the one or more energy consuming sites to auser; and the contextual information comprises: a geographicdistribution; a distribution by size; a distribution by aggregatedclimate zone; and/or a distribution across virtually all sites to revealconsumption in an overall context of an enterprise of the sites.